Ideological Support for the Indian Caste System: Social Dominance Orientation, Right-Wing Authoritarianism and Karma
Bibliographic record
Abstract
This paper extends the social dominance perspective to the Indian context by examining the role of belief in Karma (sanchita) in the justification of the Indian caste system. Using social dominance theory (Sidanius & Pratto, 1999) and the dual process model (Duckitt, 2001) as guiding theoretical frameworks, we tested four related hypotheses within a sample of 385 Indian university students. In particular we expected that social dominance orientation (SDO) and right-wing authoritarianism (RWA) would both make relatively strong and independent contributions to participants’ endorsement of Karma (H1), as well as their support for antiegalitarian social policies and conventions (H2). We also predicted that endorsement of Karma, itself, would be strongly related to support for these policies, net of the influence of SDO, RWA, as well as generalized prejudice (H3). Finally, and consistent with the notion that Karma functions as a legitimizing ideology, we hypothesized that it would at least partially mediate, net of generalized prejudice, the relationships between SDO and RWA, on the one hand, and antiegalitarian and conventional social policies, on the other (H4). Results of latent variable structural equation modeling provided support for all four hypotheses. The theoretical implications of these findings are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".